Predicting rare events is difficult to model using traditional techniques. Most traditional techniques require balanced datasets to produce an accurate model. In other words, the model construction technique requires approximately equal numbers of target events and non-target events. This is a problem for trying to predict rare events, where the target event does not occur as often as the non-target events. Other statistical models usually look for correlations between historical variables and the outcome. These models typically do not take into account the impact of the order in which the variable changes occur. Often times, the most relevant event for prediction is a rare event, and current modeling techniques have difficulty modeling such infrequent events.